Vitamin D supplementation trial in infancy: body composition effects at 3 years of age in a prospective follow‐up study from Montréal
Bibliographic record
Abstract
Summary Background The impact of vitamin D status on body composition is not well understood. Objectives Evaluate how vitamin D supplementation in infancy affects body composition at 3 years of age. Methods Double‐blind randomized trial of 132, 1‐month‐old healthy, breastfed infants randomly assigned to receive oral vitamin D3 supplements of 400, 800, 1200 or 1600 IU d−1 for 11 months. In the present analysis, 87 (66%) returned at 3 years of age. Body composition was measured using dual‐energy x‐ray absorptiometry and plasma 25‐hydroxyvitamin D [25(OH)D] concentrations by liquid chromatography tandem mass spectrometry. Results Anthropometry, body composition, diet, activity and demographics were similar across dosage groups at 3 years. Mean 25(OH)D concentration from 1 month to 3 years was higher (P < 0.001) in the 1200 IU group than 800 and 400 IU groups. Children with 25(OH)D concentrations above 75 nmol L−1 had lower fat mass (~450 g; P = 0.049). In multiple linear regression, mean 25(OH)D was associated with lean mass percent (β = 0.06; CI: 0.00, 0.12; P = 0.042), fat mass (β = −11.29; CI: −22.06, −0.52; P = 0.048) and body fat percent (β = −0.06; CI: −0.12, −0.01; P = 0.045). Conclusions Higher vitamin D status from infancy through to 3 years of age associates with leaner body composition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".